Thermal Management of an Air-Cooled PEM Fuel Cell: Cell Level Simulation
Bibliographic record
Abstract
Air-cooled polymer electrolyte membrane (PEM) fuel cells have recently been the center of attention mainly because of the simplicity they bring into the fuel cell industry. Their main advantage is the elimination of balance-of-plant subsystems such as the liquid coolant loop, heat exchanger, compressor, and air humidifier which greatly reduces the complexity, parasitic power, and cost of the overall system. In air-cooled fuel cells, air is used as a combined oxidant and coolant. However, the net power output is limited by the heat rejection rate and the overall performance and durability are restricted by high temperature gradients during stack operation. An important initial step toward this goal is accurate knowledge of the temperature distribution in the stack in order to optimize heat removal by suitable thermal management strategies. In the present study, a three dimensional numerical model is developed that can predict the temperature distribution in cell level with an acceptable accuracy. Using this methodology, the maximum temperature in the stack as well as temperature gradients, which are two essential operating parameters for air-cooled fuel cells, can be obtained. The model is validated using experimental data for the 1020ACS fuel cell stack from Ballard Power Systems. A parametric study is performed for bipolar plate thermal conductivity and overall thermal characteristics on the cell level to examine the effects of these parameters on the maximum stack temperature, temperature gradient in the cell, and overall heat rejection rate. Based on these results, recommendations are provided for improved thermal design of air-cooled fuel cells.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".